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Advances in Politics and Economic 
ISSN 2576-1382 (Print) ISSN 2576-1390 (Online) 

Vol. 2, No. 1, 2019 
www.scholink.org/ojs/index.php/ape 

51 
 

Original Paper 

Can One Promote a Delegation of the Bilateral Aid to 

Multilateral Donors to Improve Foreign Aid Effect on Economic 

Growth in ECOWAS (Note 1) Countries? 

W. Jean Marie Kébré1* 
1 Ministry of Economy, Finance and Development, University Ouaga 2, Burkina Faso 
* W. Jean Marie Kébré, Ministry of Economy, Finance and Development, University Ouaga 2, Burkina 

Faso 

 

Received: February 3, 2019   Accepted: February 20, 2019    Online Published: February 27, 2019 

doi:10.22158/ape.v2n1p51              URL: http://dx.doi.org/10.22158/ape.v2n1p51 

 

Abstract 

This article analyzes the relationship between external aid and economic growth in the ECOWAS 

region, with a focus on bilateral and multilateral aid effects. The key idea behind this analysis is an 

argument of Svensson (2000) that multilateral aid is more effective than bilateral aid because of the 

high degree of altruism of bilateral donors. He therefore suggested a delegation of bilateral aid to 

multilateral institutions. To appreciate his suggestion, this analysis used panel data from the 16 

ECOWAS countries from the period 1984 to 2014. The results of the estimates, based on the dynamic 

least squares estimator (DOLS), show a negative effect of foreign aid on economic growth. This 

negative effect on economic growth persists when the components of aid are introduced into the model. 

In addition, results highlight that governance is a channel through which foreign aid affect positively 

economic growth. In these conditions, bilateral aid is more effective on economic growth than 

multilateral aid. These results about foreign aid received by ECOWAS countries invalidates Svensson’s 

(2000) theory. Therefore, a delegation of bilateral aid to multilateral institutions is not relevant 

because bilateral aid contributes more to economic growth if governance is taken into account. 

Keywords 

bilateral aid, multilateral aid, economic growth, governance 

 

 

 

 



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1. Introduction 

Foreign aid in economics theory is considered as an additional resource to domestic resources in order 

to finance economic growth. In Harrod-Domar growth model, for example, foreign resources allow to 

increase investment that leads to increased production. But, as Chenery and Strout (1966) pointed out 

in their two-gap growth model, achievement of a growth objective depends on investments efficiency. 

Taking these factors into account, many studies have examined the issue of foreign aid effectiveness in 

terms of contribution to economic growth in recipient countries. The results of these studies are 

strongly discussed and three major trends emerge from economics literature. 

While some studies concluded that aid does not affect or even negatively affects economic growth 

(Boone, 1994; Easterly, 2003; Easterly, 2008; Gyimah-Brempong et al., 2012; Moyo, 2008), others 

showed a positive relationship between aid and growth (Arndt et al., 2015; Clemensn et al., 2004; 

Galiani et al., 2014; Lof et al., 2015) with a decreasing marginal return of aid (Collier & Dollar, 2001; 

Dalgaard et al., 2004; Durbarry et al., 1998; Hansen & Tarp, 2001). Others else concluded that aid is 

only effective at supporting positive economic growth when recipient countries adopt good economic 

policies (Burnside & Dollar, 2000; Burnside & Dollar, 2004; Chauvet & Guillaumont, 2003). 

The Economic Community of West African States (ECOWAS) is a zone whose member countries have 

received foreign aid since their independence. From 1980 to 2016, these countries received aid of about 

14.78% on average their GDP. During the same period, they estimated their GDP per capita of about 

585.07 US dollars with an average evolution of about 2.57% per year. Despite the increased evolution 

of the GDP per capita, it is not clear that foreign aid contributed on it. Indeed, Figure 1 in appendix 

established the relationship between foreign aid and GDP per capita. From this Figure, the trend 

emerging is a negative correlation between these two economic variables. 

Given aid flows received and the relationship between aid and GDP per capita established by the scatter 

plots, and according to the ongoing debate in empirical literature on the issue, we questions the ability 

of foreign aid to promote economic growth in ECOWAS countries. To this main question we can add 

these subsidiary questions. 

1.1 Does Foreign Aid Effect on Economic Growth Depend on Donors? 

This question draws its meaning from the debate about aid effectiveness depending on whether it 

comes from a bilateral or multilateral donor. Indeed, in his model, Svensson (2000) was looking for 

incentive mechanisms to make external aid more effective. He conducted his analysis as a strategic 

game form between donor and recipient, focusing on the problem of moral hazard that affects aid 

efficiency. He concluded that aid from multilateral donors was more effective than that from bilateral 

donors because of their high degree of altruism. Svensson (2000) therefore suggested that bilateral 

donors delegate their aid to multilateral institutions. 

This degree of altruism is expressed mainly through bilateral donors aversion to poverty and their aid 

allocations which strongly depend on their strategic interests (Raschky & Schwindt, 2012). In contrast, 

multilateral institutions are less averse to poverty. Because of their aim, these organizations are focused 



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on economic performance and their aid allocations depend on it (Hagen, 2006; Torsvik, 2005). 

Most time, studies separately analyze the effects of bilateral and multilateral aid without necessarily 

coming to a comparative assessment of their effectiveness on economic growth. Very few authors, to 

our knowledge, have been interested in a comparative analysis of bilateral and multilateral aid 

effectiveness. In this respect, Lessmann and Markwardt (2010) in a classic growth model performed a 

comparative analysis and highlighted differences in effectiveness between bilateral and multilateral aid. 

According to their results, and contrary to Svensson’s results, bilateral aid has a slightly higher effect 

on economic growth than multilateral aid. Wako (2011), using panel data of 42 Sub-Saharan African 

countries for the years 1980 through 2007, appreciated the effect of bilateral and multilateral aid on 

economic growth of these countries. He found that there was no evidence for the (conditional or 

unconditional) effectiveness of both kinds of aid. According to him, bilateral or multilateral aid on their 

own, or in interaction with policy, is ineffective at enhancing economic growth, regardless of whether 

one measures it relative to the recipients’ gross domestic product or in per capita terms. Other authors, 

interested in the issue, went beyond the source of aid and appreciated donors’ own policies for effective 

allocation of aid (Dreher et al., 2015; Gary & Maurel, 2015; Minasyan et al., 2017). The main 

conclusion from their studies is that more coherent donors’ policies are associated with stronger 

economic growth in recipient countries. 

1.2 Does Aid Effectiveness Depend on the Quality of Governance in ECOWAS Countries? 

The interest of this question comes from economic debates on non-linear influence of aid on economic 

growth. This non-linearity results on the one hand from the presence of transmission channels of aid 

effects (Chenery & Strout, 1966; Burnside & Dollar, 2000) and on the other hand from the capacity of 

recipient countries to absorb foreign aid (Collier & Dollar, 2001). 

Several authors have tested the presence of transmission channels in their analysis of aid effect on 

economic growth. Already, Burnside and Dollar (2000) in their study showed that aid would only be 

effective on economic growth in countries with good policies and institutional quality. Several studies 

have undertaken, with varying degrees of success, to confirm these results (Collier & Hoeffler, 2002; 

Kosack, 2003; Mosley, 2015). In addition, Collier and Dehn (2001) also found that external shocks 

could also influence aid effect on growth. In this sense, a group of studies concluded that aid was 

effective in countries exposed to macroeconomic fluctuations and large climatic variations (Dalgaard et 

al., 2004; Hudson, 2015), and foreign aid would contribute to mitigate these external shocks effects. 

Regarding absorptive capacity, studies pointed out that too much aid could compromise its efficiency 

on economic development of recipient countries. In this regard, Hansen and Tarp (2001) has shown that 

marginal returns of aid become negative when these flows exceed 25% of GDP, while Lensink and 

White (2001) has set this threshold around 40%. As for Gyimah-Brempong et al. (2012), they found 

that aid effect on economic growth in 77 developing countries is positive only if the level of aid was 

between 6.6 and 14.4% of GDP. 

 



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The literature on aid effectiveness remains extremely rich and varied. The debate on aid effects is so 

controversial that we can said that there are as many papers exposing a positive relationship between 

aid and growth than papers supporting a negative effect or no significant effect on income growth 

(Doucouliagos & Paldam, 2009). This heterogeneity highlights the lack of consensus on aid effects on 

economic growth. 

The purpose of this study is to empirically examine the relationship between economic growth and 

foreign aid, especially taking into account its two main sources: bilateral and multilateral aid. The idea 

is to investigate the effects of bilateral and multilateral aid both in order to assess the complementarity 

that could result from these two modes of aid delivery. This study also examines the role of governance 

in the relationship between external aid and economic growth. Based on this literature, the following 

hypotheses will be tested: 

 Multilateral aid is more efficient to economic growth than bilateral aid; 

 Aid has decreasing marginal effect on economic growth and the quality of governance is a 

channel that improves aid effect. 

As previously reported, the relationship between foreign aid and economic growth has attracted much 

interest in the economic literature. However, very few studies, to our knowledge, have made a 

comparative empirical analysis of the effect by source of aid, especially in ECOWAS. The essential 

contribution of this paper lies in this comparative analysis while appreciating the role of governance. 

 

2. Materials and Methods 

The econometric analysis is based on a traditional neoclassical growth model in open economy, derived 

from the Cobb-Douglas type function. This kind of model is widely used in the empirical literature on 

aid effectiveness. Thus, from this function, Hansen and Tarp (2001) developed a model of aid analysis 

in which income growth depends on aid, investment, and policy variables. This model is used by 

Dalgaard et al. (2004) and Gyimah-Brempong et al. (2012) to analyze the impact of aid on growth in 

developing countries. The results concluded that there was a direct and quadratic relationship between 

aid and income growth and an indirect relationship through investment and through economic policies. 

This analysis of the impact of aid on economic growth in ECOWAS countries draws on this model. The 

interest in using this model is that it allows to take into account the quadratic form of aid and allows aid 

to interact with governance. 

2.1 Model Specification 

With reference to aid-growth literature, the basic equation of economic growth to investigate as 

follows: 

 , 0, 1, , 2, , 3, , ,           1i t i i i t i i t i i t i t i ty A X Z              

Where subscripts “i” and “t” respectively refer to country and to time. “A” defines foreign aid (as a 

percentage of GDP) which is split into bilateral and multilateral component and “Z” represents 



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governance variable. “X” is a vector of control variables. ,,   and i t i t   respectively capture the 

individual-specific effects, the time-specific factors and idiosyncratic error. 

In equation (1), aid is considered as an exogenous variable explaining the growth rate of real GDP per 

capita. However, it is important to point out the complex environment in which aid is allocated and 

analyzed. This complexity, highlighted in Burnside and Dollar’s (2000) study, is explained by the fact 

that aid must be allocated to countries with good institutions. Under such conditions, the possibility of 

endogeneity problem is very likely when estimating relationship between foreign aid and growth. 

Therefore, it would be cautious to take into account the endogenous nature of aid. Moreover, the 

observation of Figure 2 in the appendix, which relates aid to per capita income, shows a non-linear 

dynamic of the general trend curve resulting from scatter plots. Such dynamic suggests a threshold 

effect of aid that it would be interesting to test in this analysis. That is why aid, in equation (2), is 

introduced in quadratic form and interact with governance. 

   2
, 0, 1, , 2, , 3, , , 4, , ,*          2i t i i i t i i t i i t i t i i t i t i ty A A Z A X              

 

2.2 Estimation Issues and Procedures 

The model (2) as presented is a static approach to investigated phenomenon. This approach does not 

allow to take into account the possibility of a dynamic dimension of the phenomenon. This dynamic 

dimension is a significant issue in the relationship between foreign aid and economic growth. Indeed, 

in the policies of aid allocation, it’s important to note that donors integrates the level of GDP per capita 

of recipient countries. So foreign aid tends to go to low-income countries. There is therefore a causality 

“income-aid” well-established in economics literature: lower is the country’s income, more aid it 

receives. The study here is about the causality “aid-income”: does aid allow to increase income? The 

possibility of endogeneity problem therefore is very likely that estimation method must take into 

account. 

Conventional estimators (fixed/random effects estimators) that impose the homogeneity of the 

estimated parameters are not appropriate for equation (2) because they can be seriously biased (Pesaran 

& Smith, 1995). Besides, the problem of endogeneity (possibility of double correlation between aid and 

economic growth) needs to be adequately addressed in order to achieve robust estimators. The most 

widely used techniques that take into account these econometric problems are: the Fully Modified (FM) 

and Dynamic Least Squares (DOLS) estimators developed by Chiang and Kao (2002), the error 

correction estimators proposed by Pesaran and Smith (1995) namely Pooled Mean Group (PMG) and 

Mean Group (MG). 

This study adopts the DOLS estimator because of its superiority (best estimators) on the FM estimator. 

According to Chiang and Kao (2001), on small samples (relatively small size), the DOLS estimator 

provided a better correction of long-term endogeneity bias than the FM estimator. This estimator is an 

extension of Stock and Watson’s (1993) one. To obtain an unbiased estimator of the long term 



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parameters, the DOLS method estimates a parametric correction by including lagged levels as well as 

lagged differences of variables. In other words, the technique consists in including leads and lags of 

,i tX
 

in the cointegration relationship in order to remove the correlation between the explanatory 

variables and the error term. 

Thus, considering the equation (2) and assuming the presence of non-stationary variables, the DOLS 

estimator is provided by the following equation: 

   
2

1

, 0 1 , 2 , , , 1 , ,        3
k q

i t i t i t i k i t k i t k i t
k q

y y M y M    


  


         

In this equation (3), ,i tM represents the set of explanatory variables other than , 1i ty  . ,ki  
is the 

estimated parameter of anticipation or delay in first difference of the explanatory variables. 

2.3 Description of Variables and Data 

The estimation uses four categories of variables for the analysis: 

 The economic growth appreciated by the growth rate of real GDP per capita. 

 Foreign aid measures the amount of external resources received as official development 

assistance (Aid/GDP). Over the period 1980 to 2016, aid to ECOWAS countries represented about 

14.78% of GDP per year. This aid split up into bilateral and multilateral aid. Bilateral donors are 

represented by single country agencies that provide aid directly to developing countries or NGOs. 

Alternatively, multilateral donors exist where more than two bilateral donors pool their aid flows and, 

through the international organization’s own decision processes that aggregate the member countries’ 

preferences, then provide the aid to developing countries or NGOs. During this period, the most 

important part of this foreign aid was delivered by bilateral donors (57.76%), while multilateral donors 

delivered about 42.24%. Figure 1 below shows the evolution of these two components of foreign aid 

delivered to ECOWAS countries. The main information from this Figure is that, from the 1996s, there 

has been a pronounced fall in the real amount of foreign aid provided by donors for these developing 

countries. Economics literature attributes this pronounced decreasing to the aid fatigue. 



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Figure 1. Bilateral and Multilateral Aid to ECOWAS Countries, 1980-2016 

 

 Governance denotes institutional variables. Many institutions attempted to develop indicators to 

assess governance in countries. Among these institutions, there are the Country Policy and Institutional 

Assessment (CPIA) developed the World Bank since 1996, and the International Country Risk Guide 

Series (ICRG) which began publication in 1984. This are the latter indicators that are used in this study. 

These indicators are derived from expert surveys of economic and political conditions in about 140 

countries. This study uses the ICRG indicators. The choice for these indicators results from the fact that 

the series are longer than those of other institutions. For the analysis, the governance variable is 

obtained by summing the scores of the 12 ICRG indicators whose maximum score for a country is 100. 

Figure 3 shows the overall level of institutional quality in the ECOWAS countries (Note 2). From 1984 

to 2014, ECOWAS countries was characterized by a low level of governance with an average score 

estimated at 51.69. Nevertheless, there is an improvement in governance during this period because the 

score increased from 45.93 in 1984 to51.75 in 2014. 

 



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Figure 2. Governance in ECOWAS Countries, 1984-2014 

 

 Control variables relate to investment and financial development valued by the broad money (all 

as a percentage of GDP). They also concern the human capital measured by the active population (as a 

percentage of the total population) and the gross enrollment rate, the economic stability appreciated by 

the rate of inflation. 

We use quantitative data for this analysis coming from three sources. The data relating to foreign aid 

are drawn from the Organization for Economic Co-operation and Development (OECD) database. The 

data about governance comes from the IRCG database. For control variables, they come from the 

World Bank database. 

 

3. Results and Discussions 

The effects of foreign aid (and split into bilateral and multilateral aid) on economic growth has been 

estimated through the economic growth model presented above. The results relate to stationarity and 

cointegration tests on the one hand and regressions on the other. 

3.1 The Results of Unit Root and Cointegration Tests 

To determine the order of integration of variables and examine the presence of a long-term relationship 

between them, the first step of the empirical approach was to perform unit root and cointegration tests. 

For this purpose, two tests developed by Levin et al. (2002) and by Im et al. (2003) were used to assess 

the non-stationarity of the variables. 

The first test, imposing the assumption of homogeneity of the autoregressive root, assume, as null 

hypothesis, a unit root for all the individuals of the panel versus the hypothesis of the absence of unit 

root for the set of individuals. Under these conditions, it is unlikely that in case of rejection of the null 

hypothesis, we can accept the hypothesis of an autoregressive root common to all individuals. 

 



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The second test responds to this concern by considering a model with individual effects and no 

deterministic trend. It postulates the unit root versus the possibility of cohabitation of two categories of 

individuals in the panel. Individuals for whom the variable is stationary and those for whom it’s not. 

The results of implementing of these two tests are presented in the following Table. 

 

Table 1. Results of Unit Root Tests Applied to the Variables 

Variables LL  IPS  

 Coefficient P-value Coefficient P-value 

GDP Growth** −0.159 0.366 −1.659 0.989 

Aid* −0.491 0.000 −3.172 0.000 

Bilateral Aid* −0.650 0.000 −3.864 0.000 

Multilateral Aid* −0.640 0.000 −3.760 0.000 

Money* −0.422 0.000 −3.206 0.000 

Inflation* −0.667 0.000 −3.841 0.000 

Investment* −0.321 0.001 −2.542 0.050 

Active pop** −0.108 0.000 −2.471 0.101 

Education** −0.112 0.995 −1.869 0.910 

Governance** −0.213 0.135 −2.099 0.630 

Democraty** −0.188 0.362 −1.860 0.919 

Corruption** −0.244 0.171 −2.143 0.553 

Source: Author. 

IPS = IM-Pesaran-Shin’s test; LL = Levin-Lin-Chu’s test; Stationary at level (*), in first difference 

(**). 

 

The results of the unit root tests show that GDP growth and governance indicators (governance, 

democracy and corruption) are stationary in first difference, while the foreign aid (total aid, bilateral 

and multilateral aid) are stationary at level. For the control variables, investment, broad money and 

inflation are stationary at level; the active population and education being stationary in first difference. 

In order to highlight the long-term relationship between the variables and based on the results of the 

panel unit root test, we use the cointegration tests in panel developed by Westerlund (2007). The tests 

apply to variables that are integrated of order one. The underlying idea is to test the absence of 

cointegration while determining whether each of the individuals in the panel can adopt an error 

correction model. For this, one considers an error correction model in which the parameter ia
 

represents the speed of adjustment towards the long-term equilibrium. The model consists of four tests: 

Gt, Ga, Pt and Pa. The first two tests are called group mean tests, and the alternative hypothesis is that 

at least one observation has cointegrated variables. The last two tests are named panel tests and in this 



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case, the alternative hypothesis is that the panel, considered as a whole, is cointegrated. The results are 

presented in the following Table. 

 

Table 2. Results of Cointegration Tests 

Test Value z-Value P-robust Value 

Gt* −1.316 3.509 0.053 

Ga*** −0.711 5.256 0.008 

Pt 2.635 9.046 0.925 

Pa** 0.689 4.481 0.028 

Source: Author. 

(***), (**) and (*) = significant respectively at 1%, 5% and 10%. 

 

The results in Table 2 show that the non-cointegration hypothesis is rejected for all statistics except for 

Pt. Considering these results, it can reasonably be concluded that, for part of the sample, the variables 

are notsignificant. 

3.2 Discussion of Estimates Results 

The second step of the empirical analysis was to estimate the economic growth model using the DOLS 

estimator. Estimates have been made taking into account total foreign aid received and its components 

whom are bilateral and multilateral aid. The results reported in Tables 3 and 4 are generally satisfactory. 

Indeed, the Chi tests are significant and the gradual introduction of the interest variables shows a 

certain stability of the model; which is a signal of the estimates robustness. The results from these 

estimates lead to the following conclusions. 

3.2.1 Foreign Aid Is Harmful to Economic Growth, Whether Bilateral or Multilateral 

This conclusion is derived from the results estimated of the basic model provided by columns (I) of 

Table 3. These results, which allow to assess the direct effects of aid, show that foreign aid has negative 

effects on economic growth. For example, a unit increase (as a percentage of GDP) of aid leads to a 

drop in economic growth of 0.064%. These results confirm previous studies of authors who found that 

foreign aid negatively affects the economic growth of recipient countries (Easterly, 2003; 

Gyimah-Brempong et al., 2012; Moyo, 2008). But in looking at its components, the results are only 

significant in multilateral aid. These first results make the validity of the conclusion of Svensson (2000) 

in ECOWAS countries rather subtle. It could be accepted in the sense that only multilateral aid has a 

significant effect on economic growth. However, its negative effects limits the scope of Svensson’s 

thesis in ECOWAS. 

Another interesting result emanating from columns (II) is the non-validation of the threshold effect in 

the relationship between foreign aid and economic growth. Indeed, the introduction of the quadratic 

variable of aid (also for bilateral and multilateral aid) modifies the sense of the relationship between aid 



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and economic growth, but the coefficients are notsignificant. 

 

Table 3. Results of the Estimates of Foreign Aid Effects on Economic Growth 

Variable 
Total Foreign Aid Bilateral Aid Multilateral Aid 

(I) (II) (I) (II) (I) (II) 

Investment 
0.22*** 

(2.36) 

2.22*** 

(2.48) 

0.21** 

(2.11) 

0.23*** 

(2.40) 

0.21** 

(2.26) 

0.19** 

(2.19) 

Active Pop 
-0.15 

(-0.70) 

-0.14 

(-0.71) 

-0.15 

(-0.66) 

-0.14 

(-0.65) 

-0.15 

(-0.72) 

-0.13 

(-0.66) 

Education 
0.10*** 

(2.37) 

0.09*** 

(2.33) 

0.10** 

(2.25) 

0.10** 

(2.19) 

0.10*** 

(2.37) 

0.08** 

(2.14) 

Aid 
-0.064** 

(-1.94) 

-0.08 

(-1.00) 

-0.08 

(-1.30) 

-0.19 

(-1.29) 

-0.15*** 

(-3.12) 

-0.19* 

(-1.75) 

SquaredAid   
0.003 

(0.50)  

0.001 

(1.08)  

0.001 

(1.27) 

Money 
-0.15*** 

(-2.83) 

-0.14*** 

(-2.82) 

-0.15*** 

(-2.60) 

-0.14*** 

(-2.56) 

-0.14*** 

(-2.68) 

-0.13*** 

(-2.60) 

Inflation 
-0.07* 

(-1.82) 

-0.05 

(-1.48) 

-0.07* 

(-1.74) 

-0.06 

(-1.51) 

-0.07* 

(-1.83) 

-0.06 

(-1.54) 

Governance 
0.18*** 

(2.56) 

0.16*** 

(2.37) 

0.21*** 

(2.80) 

0.19*** 

(2.57) 

0.17** 

(2.31) 

0.15** 

(2.28) 

Wald Chi2 47.04*** 43.66*** 39.81*** 37.86*** 50.91*** 37.78*** 

Number of Countries 11 11 11 11 11 11 

Number of Observations 297 297 297 297 297 297 

Source: Author. 

1) provides results of the basic model estimate giving the direct effect of aid on economic growth; 

2) provides the results of the non-linear model estimates (squared aid); 

(***; **; *) indicate that the variable is significant at 1%, 5% or 10% respectively. 

 

3.2.2 Governance Is a Channel That Improves Foreign Aid Effect on Economic Growth 

To appreciate the role of governance, it has been assumed to be a channel through which foreign aid 

affected economic growth. For this purpose, the governance variable was introduced into the model as 

an interacted variable with aid. Columns (III) and (IV) of Table 4 present the results of this test. The 

results in column (III) indicate positive and significant effects at 1% of interaction variables. They 

highlight that governance is a channel through which foreign aid (both bilateral and multilateral aid) 

positively affects economic growth in ECOWAS countries. They thus corroborate the results of 



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Burnside and Dollar (2000; 2004), which showed that aid is effective on economic growth only in 

countries with good governance. These results also indicate that the correlation resulting from the 

interaction between bilateral aid and governance is stronger than that with multilateral aid. Such result 

leads to the conclusion that, in a good governance environment, bilateral aid has a higher positive effect 

on economic growth than multilateral aid. This conclusion is confirmed by the specific governance 

indicators used. Indeed, the results in columns (IV) present democracy and corruption variables crossed 

with foreign aid (total, bilateral and multilateral aid). They indicate a positive and significant 

correlation with economic growth, except for the interaction variable between multilateral aid and 

corruption. They also point out that the interaction variables between bilateral aid and governance 

indicators are stronger correlated with economic growth. These results suggest that by taking into 

account governance, Svensson’s (2000) idea about the predominance of multilateral aid effect (as 

opposed to bilateral aid effect) on economic growth could not be validated in ECOWAS countries. On 

the contrary, it could be argued that good governance contributes to eliminate the aid negative effect on 

economic growth resulting of the basic model estimates and to minimize the altruism negative effect of 

bilateral donors on their aid effectiveness. 

 

Table 4. Results of Regressions of Aid Effects with Emphasis on the Role of Governance 

Variables 
Total Foreign Aid Bilateral Aid Multilateral Aid 

(III) (IV) (III) (IV) (III) (IV) 

Investment 0.12* (1.72) 0.14** (1.96) 0.15 (1.46) 0.19* (1.84) 0.08 (0.97) 0.18** (2.02) 

Active Pop -0.10 (-0.64) -0.08 (-0.52) -0.09 (-0.40) -0.08 (-0.39) -0.12 (-0.60) -0.10 (-0.53) 

Education 0.06** (2.12) 0.05* (1.76) 0.06 (1.28) 0.05 (1.11) 0.08** (2.08) 0.06* (1.70) 

Aid -0.52*** (-8.38) -0.44*** (-9.00) -0.86*** (-7.25) -0.92*** (-8.27) -0.55*** (-7.66) -0.50*** (-7.62) 

Money -0.15*** (-3.68) 0.11*** (-2.77) -0.08 (-1.39) -0.05 (-0.95) -0.10** (-2.13) 0.07* (-1.71) 

Inflation -0.03 (-1.14) -0.04 (-1.33) -0.02 (-0.47) -0.03 (-0.73) -0.05* (-1.44) -0.01 (-0.44) 

Aid*governance 0.009*** (7.82) 
 

0.009*** (7.64) 
 

0.005*** (5.63) 
 

Aid*corruption   0.07*** (2.99) 
 

0.10*** (2.82) 
 

0.0008 

(0.03) 

Aid*democraty   0.04*** (3.84)   0.05*** (3.17)   0.04*** (2.92) 

Wald Chi2 120.46*** 132.13*** 76.46*** 108.72*** 72.71*** 74.03*** 



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Number of countries 11 11 11 11 11 11 

Number of observations 297 297 297 297 297 297 

Source: Author. 

(III) and (IV) provide results of the non-linear model estimates (foreign aid interacting with governance 

indicators);  

(***; **; *) indicate that the variable is significant at 1%, 5% or 10% respectively. 

 

4. Conclusion 

The purpose of this article was to evaluate foreign aid effect on the economic growth in the ECOWAS 

countries, especially taking into account the components of this aid (bilateral and multilateral), and the 

role of governance. Empirical results based on a dynamic panel data approach indicate a negative 

correlation between foreign aid and economic growth, regardless of component of aid. Testing the 

possibility of a reversal effect, the results indicate a modification of sign of the squared aid but its 

coefficient is not significant. In these conditions, the hypothesis of the presence of threshold effects in 

the correlation between aid and economic growth cannot be validated because of the non-significance 

of the squared aid coefficient. 

In addition, the introduction of governance as an interaction variable with foreign aid shows a positive 

effect on economic growth. This result constitutes the signature that governance can be considered as a 

channel through which foreign aid has positive effect on economic growth. And this positive channel is 

maintained with the governance indicators used that are corruption and democracy. With these results, 

we note that contrary to the theory of Svensson (2000), bilateral aid has a stronger correlation with 

economic growth than multilateral aid in ECOWAS economies if governance is taken into account in 

the analysis. These different results suggest that ECOWAS countries have an interest in focusing on 

governance as it is a channel to improve foreign aid effect on economic growth. The idea of delegating 

bilateral aid to multilateral institutions as suggested by Svensson (2000) therefore does not seem 

relevant. On the contrary, the results suggest a reverse delegation, in a good governance environment. 

 

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Notes 

Note 1. Economic Community of West African States. 

Note 2. Two ECOWAS countries (Benin and Cape Verde) do not appear in the ICRG database. 

 

Appendix 1 

Linking the Foreign Aid Evolution with That of the GDP per Capita over the Period 1980-2016 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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Appendix 2  

Highlighting of the Non-Linear Relationship between Foreign Aid and GDP per Capita over the 

Period 1980-2016 

 

 

 


